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Direct Physical Interaction

B. Flying Mini Copter in a Tracking System

B.4. Direct Physical Interaction

Figure B.5.: Human robot interaction by direct physical interaction, in this case mediated by pushing

B.4. Direct Physical Interaction

A prominent vision is to facilitate interaction between flying robots and humans, that goes beyond device-based remote control but instead emphasizes direct interaction with the robot. This provides a basis for working together with the robot, teaching it by demonstration and physically correcting its mistakes.

Since the setup is working with a tracking system, interaction could be achieved by track- ing the human as done in motion capturing systems and working with the modeled data. Other alternatives would be to use a depth-sensing device like Kinect of a stereo camera. The most intuitive way of interaction, however, is direct physical interaction.

This is not only the most intuitive way of interacting with a robot, it also does not require additional hardware, as opposed to also tracking the human or using an RGB-D camera. For this to work, safety is imperative in order to ensure the health of the human and the physical integrity of the robot. An example interaction is depicted in fig. B.5.

The error as determined by the control loop can be used to detect sufficiently large offsets in position, which are due to external forces inflicted on the system by physical contact. A more sensitive alternative is the use of more advanced internal models, and detecting the interaction by monitoring the prediction errors of the forward model. These methods allow us to change the setpoints of the controllers and to freely position the robot in 3D space.

Bibliography

Abaid, N., Marras, S., Fitzgibbons, C., and Porfiri, M. (2013). Modulation of risk-taking be- haviour in golden shiners (notemigonus crysoleucas) using robotic fish. Behavioural pro- cesses, 100:9–12.

Adler, J. (1966). Chemotaxis in bacteria. Science, 153:708–716.

Agmon, N. and Peleg, D. (2006). Fault-tolerant gathering algorithms for autonomous mobile robots. SIAM Journal on Computing, 36(1):56–82.

Andrieu, C., Doucet, A., and Holenstein, R. (2010). Particle markov chain monte carlo meth- ods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(3):269–342. Asimov, I. (1950). I, ROBOT. Gnome Press.

Atanasov, N., Le Ny, J., Michael, N., and Pappas, G. J. (2012). Stochastic source seeking in complex environments. In Robotics and Automation (ICRA), 2012 IEEE International Conference on, pages 3013–3018. IEEE.

Ballerini, M., Cabibbo, N., Candelier, R., Cavagna, A., Cisbani, E., Giardina, I., Orlandi, A., Parisi, G., Procaccini, A., Viale, M., et al. (2008). Empirical investigation of starling flocks: a benchmark study in collective animal behaviour. Animal behaviour, 76(1):201–215. Baranes, A. and Oudeyer, P.-Y. (2013). Active learning of inverse models with intrinsically

motivated goal exploration in robots. Robotics and Autonomous Systems, 61(1):49–73. Barbosa, M., Bernardino, A., Figueira, D., Gaspar, J., Gonçalves, N., Lima, P. U., Moreno, P.,

Pahliani, A., Santos-Victor, J., Spaan, M. T., et al. (2009). Isrobotnet: A testbed for sensor and robot network systems. In Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on, pages 2827–2833. IEEE.

Barsalou, L. W. (2008). Grounded cognition. Annu. Rev. Psychol., 59:617–645.

Barsalou, L. W. (2009). Simulation, situated conceptualization, and prediction. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1521):1281–1289.

Barsalou, L. W. (2010). Grounded cognition: past, present, and future. Topics in Cognitive Science, 2(4):716–724.

Bekmezci, I., Sahingoz, O. K., and Temel, Ş. (2013). Flying ad-hoc networks (fanets): a survey. Ad Hoc Networks, 11(3):1254–1270.

Beni, G. (2005). From swarm intelligence to swarm robotics. In Swarm Robotics, pages 1–9. Springer.

Bentley, J. L. (1975). Survey of techniques for fixed radius near neighbor searching. Technical report, Stanford Linear Accelerator Center, Calif.(USA).

Berg, H. C. and Brown, D. A. (1972). Chemotaxis in escherichia coli analysed by three- dimensional tracking. Nature, 239(5374):500–504.

Bergstra, J., Yamins, D., and Cox, D. D. (2013). Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms. In van der Walt, S., Millman, J., and Huff, K., editors, Proceedings of the 12th Python in Science Conference, pages 13 – 20. Bergstra, J. S., Bardenet, R., Bengio, Y., and Kégl, B. (2011). Algorithms for hyper-parameter

optimization. In Advances in Neural Information Processing Systems, pages 2546–2554. Bjerknes, J. D. and Winfield, A. F. (2013). On fault tolerance and scalability of swarm robotic

systems. In Distributed Autonomous Robotic Systems, pages 431–444. Springer.

Blakemore, S.-J., Wolpert, D., and Frith, C. (2000). Why can’t you tickle yourself? Neurore- port, 11(11):R11–R16.

Blakemore, S.-J., Wolpert, D. M., and Frith, C. D. (1998). Central cancellation of self-produced tickle sensation. Nature neuroscience, 1(7):635–640.

Blobel, V. and Lohrmann, E. (1998). Statistische und numerische Methoden der Datenanalyse. Teubner.

Blum, C., Berthold, O., Rhan, P., and Hafner, V. V. (2014). Intuitive control of small flying robots. In Proceedings of the 2014 ACM/IEEE International Conference on Human-robot In- teraction, HRI ’14, pages 128–129, New York, NY, USA. ACM.

Blum, C. and Hafner, V. V. (2012). An autonomous flying robot for network robotics. Robotics; Proceedings of ROBOTIK 2012; 7th German Conference on, pages 1 –5.

Blum, C. and Hafner, V. V. (2013). Robust exploration strategies for a robot exploring a wireless network. Electronic Communications of the EASST, 56.

Blum, C. and Hafner, V. V. (2014). Gradient-based taxis algorithms for network robotics. arXiv preprint arXiv:1409.7580.

Blum, C. and Hafner, V. V. (2015). Active exploration of sensor networks from a robotics perspective. in preparation.

Blum, C., Winfield, A. F. T., and Hafner, V. V. (2015). Internal model based safety. in prepa- ration.

Bonabeau, E., Dorigo, M., and Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems. Proceedings volume in the Santa Fe Institute studies in the sciences of complexity. OUP USA.

Bibliography Bongard, J., Zykov, V., and Lipson, H. (2006). Resilient machines through continuous self-

modeling. Science, 314(5802):1118–1121.

Bouzid, Z., Potop-Butucaru, M. G., and Tixeuil, S. (2009). Byzantine-resilient convergence in oblivious robot networks. In Distributed Computing and Networking, pages 275–280. Springer.

Bouzid, Z., Potop-Butucaru, M. G., and Tixeuil, S. (2010). Optimal byzantine-resilient con- vergence in uni-dimensional robot networks. Theoretical Computer Science, 411(34):3154– 3168.

Braitenberg, V. (1986). Vehicles: Experiments in synthetic psychology. MIT press.

Brambilla, M., Ferrante, E., Birattari, M., and Dorigo, M. (2013). Swarm robotics: a review from the swarm engineering perspective. Swarm Intelligence, 7(1):1–41.

Briod, A., Kornatowski, P., Klaptocz, A., Garnier, A., Pagnamenta, M., Zufferey, J.-C., and Floreano, D. (2013a). Contact-based navigation for an autonomous flying robot. In Inter- national Conference on Intelligent Robots and Systems (IROS’13), pages 3987–3992.

Briod, A., Zufferey, J.-C., and Floreano, D. (2013b). Optic-flow based control of a 46g quadro- tor. In Workshop on Vision-based Closed-Loop Control and Navigation of Micro Helicopters in GPS-denied Environments, IROS’13.

Bronstein, I. and Semendjajew, K. (2008). Taschenbuch der Mathematik. Harri Deutsch. Brown, T. X., Argrow, B., Dixon, C., Doshi, S., Thekkekunnel, R.-G., and Henkel, D. (2004). Ad

hoc uav ground network (augnet). In AIAA 3rd Unmanned Unlimited Technical Conference, pages 1–11.

Buhl, J., Sumpter, D. J., Couzin, I. D., Hale, J. J., Despland, E., Miller, E., and Simpson, S. J. (2006). From disorder to order in marching locusts. Science, 312(5778):1402–1406.

Cameron, S., Hailes, S., Julier, S., McClean, S., Parr, G., Trigoni, N., Ahmed, M., McPhillips, G., De Nardi, R., Nie, J., Symington, A., Teacy, L., and Waharte, S. (2010). Suaave: Combining aerial robots and wireless networking. In 25th Bristol International UAV Systems Conference, pages 1–14.

Chaimowicz, L., Cowley, A., Gomez-Ibanez, D., Grocholsky, B., Hsieh, M., Hsu, H., Keller, J., Kumar, V., Swaminathan, R., and Taylor, C. (2005). Deploying air-ground multi-robot teams in urban environments. In Multi-Robot Systems. From Swarms to Intelligent Automata Volume III, pages 223–234. Springer.

Clark, C. M., Frew, E. W., Jones, H. L., and Rock, S. M. (2003). An integrated system for com- mand and control of cooperative robotic systems. In International Conference on Advanced Robotics.

Clement, J., Défago, X., Gradinariu Potop-Butucaru, M., Messika, S., and Raipin-Parvedy, P. (2012). Fault and byzantine tolerant self-stabilizing mobile robots gathering.

Cohen, R. and Peleg, D. (2006). Convergence of autonomous mobile robots with inaccurate sensors and movements. In STACS 2006, pages 549–560. Springer.

Conant, R. C. and Ross Ashby, W. (1970). Every good regulator of a system must be a model of that system? International journal of systems science, 1(2):89–97.

Correll, N., Bachrach, J., Vickery, D., and Rus, D. (2009). Ad-hoc wireless network coverage with networked robots that cannot localize. In Robotics and Automation, 2009. ICRA’09. IEEE International Conference on, pages 3878–3885. IEEE.

Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3):273–297. Courant, R., Friedrichs, K., and Lewy, H. (1928). Über die partiellen Differenzengleichungen

der mathematischen Physik. Mathematische Annalen, 100(1):32–74.

Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification. Information Theory, IEEE Transactions on, 13(1):21–27.

Craik, K. J. W. (1967). The Nature of Explanation. Cambridge University Press.

Cremean, L., Dunbar, W. B., van Gogh, D., Hickey, J., Klavins, E., Meltzer, J., and Murray, R. M. (2002). The caltech multi-vehicle wireless testbed. In Decision and Control, 2002, Proceedings of the 41st IEEE Conference on, volume 1, pages 86–88. IEEE.

Cruz, D., McClintock, J., Perteet, B., Orqueda, O. A., Cao, Y., and Fierro, R. (2007). Decen- tralized cooperative control-a multivehicle platform for research in networked embedded systems. Control Systems, IEEE, 27(3):58–78.

Damosso, E. (1998). Digital mobile radio: Cost 231 view on the evolution towards 3rd gen- eration systems. Final Report of the COST 231 Project.

Damosso, E. and Correia, L. M. (1999). COST Action 231: Digital Mobile Radio Towards Future Generation Systems: Final Report. European Commission.

Daniel, K., Rohde, S., Goddemeier, N., and Wietfeld, C. (2010). A communication aware steering strategy avoiding self-separation of flying robot swarms. In Intelligent Systems (IS), 2010 5th IEEE International Conference, pages 254–259. IEEE.

Dantu, K., Goyal, P., and Sukhatme, G. (2009). Relative bearing estimation from commodity radios. In Robotics and Automation, 2009. ICRA’09. IEEE International Conference on, pages 3871–3877. IEEE.

Dantu, K., Rahimi, M., Shah, H., Babel, S., Dhariwal, A., and Sukhatme, G. S. (2005). Robo- mote: enabling mobility in sensor networks. In Proceedings of the 4th international sym- posium on Information processing in sensor networks, page 55. IEEE Press.

Dasgupta, D., Yu, S., and Nino, F. (2011). Recent advances in artificial immune systems: models and applications. Applied Soft Computing, 11(2):1574–1587.

Bibliography Davidson, D. B. (2005). Computational electromagnetics for RF and microwave engineering.

Cambridge University Press.

De, P., Raniwala, A., Krishnan, R., Tatavarthi, K., Modi, J., Syed, N. A., Sharma, S., and Chi- ueh, T.-c. (2006). Mint-m: an autonomous mobile wireless experimentation platform. In Proceedings of the 4th international conference on Mobile systems, applications and services, pages 124–137. ACM.

Dearden, A. (2008). Developmental learning of internal models for robotics. PhD thesis, Impe- rial College London.

Dearden, A. and Demiris, Y. (2005). Learning forward models for robots. In Proceedings of the 19th International Joint Conference on Artificial Intelligence, IJCAI’05, pages 1440–1445, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.

Demiris, Y. (2007). Prediction of intent in robotics and multi-agent systems. Cognitive pro- cessing, 8(3):151–158.

Demiris, Y. and Dearden, A. (2005). From motor babbling to hierarchical learning by imita- tion: a robot developmental pathway. In Proceedings of the 5th International Workshop on Epigenetic Robotics, pages 31–37. Lund University Cognitive Studies.

Demiris, Y. and Khadhouri, B. (2006). Hierarchical attentive multiple models for execution and recognition of actions. Robotics and autonomous systems, 54(5):361–369.

Der, R. and Martius, G. (2006). From motor babbling to purposive actions: Emerging self- exploration in a dynamical systems approach to early robot development. In From Animals to Animats 9, pages 406–421. Springer.

Derenick, J., Fink, J., and Kumar, V. (2011). Localization using ambiguous bearings from radio signal strength. In Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on, pages 3248–3253. IEEE.

Dixon, C. (2010). Controlled mobility of unmanned aircraft chains to optimize network capacity in realistic communication environments. PhD thesis, University of Colorado at Boulder. Echeverria, G., Lassabe, N., Degroote, A., and Lemaignan, S. (2011). Modular open robots

simulation engine: Morse. In Robotics and Automation (ICRA), 2011 IEEE International Conference on, pages 46–51. IEEE.

Edlund, E., Lindgren, O., and Jacobi, M. N. (2011). Designing isotropic interactions for self- assembly of complex lattices. Physical review letters, 107(8):085503.

Elnahrawy, E., Austen-Francisco, J., and Martin, R. P. (2007). Adding angle of arrival modal- ity to basic rss location management techniques. In Wireless Pervasive Computing, 2007. ISWPC’07. 2nd International Symposium on. IEEE.

Emelyanov, S. (1970). Automatic control systems with variable structure. Technical report, DTIC Document.

Faria, J. J., Dyer, J. R., Clément, R. O., Couzin, I. D., Holt, N., Ward, A. J., Waters, D., and Krause, J. (2010). A novel method for investigating the collective behaviour of fish: intro- ducing robofish. Behavioral Ecology and Sociobiology, 64(8):1211–1218.

Fink, J. and Kumar, V. (2010). Online methods for radio signal mapping with mobile robots. In Robotics and Automation (ICRA), 2010 IEEE International Conference on, pages 1940–1945. IEEE.

Fish, R., Flickinger, M., and Lepreau, J. (2006). Mobile emulab: A robotic wireless and sensor network testbed. In IEEE INFOCOM.

Flanagan, J. R., King, S., Wolpert, D. M., and Johansson, R. S. (2001). Sensorimotor prediction and memory in object manipulation. Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale, 55(2):87.

Francis, B. A. and Wonham, W. M. (1976). The internal model principle of control theory. Automatica, 12(5):457–465.

Garcia, C. E. and Morari, M. (1982). Internal model control. a unifying review and some new results. Industrial & Engineering Chemistry Process Design and Development, 21(2):308–323. Gauss, C. F. (1823). Theoria combinationis observationum erroribus minimis obnoxiae. H.

Dieterich.

Goldsmith, A. (2005). Wireless Communications. Cambridge University Press.

Gosmann, J., Blum, C., Berthold, O., and Hafner, V. V. (2013). Tactile sensors for learning of soft landing on a flying robot. In Workshop: Sensitive Robotics, Robotics: Science and Systems 2013.

Grèzes, J., Armony, J. L., Rowe, J., and Passingham, R. E. (2003). Activations related to “mir- ror” and “canonical” neurones in the human brain: an fmri study. Neuroimage, 18(4):928– 937.

Group, I. . W. et al. (2010). Ieee standard for information technology–telecommunications and information exchange between systems–local and metropolitan area networks–specific requirements–part 11: Wireless lan medium access control (mac) and physical layer (phy) specifications amendment 6: Wireless access in vehicular environments. IEEE Std, 802:11p. Gupta, P. and Kumar, P. R. (2000). The capacity of wireless networks. Information Theory,

IEEE Transactions on, 46(2):388–404.

Halloy, J., Sempo, G., Caprari, G., Rivault, C., Asadpour, M., Tâche, F., Said, I., Durier, V., Canonge, S., Amé, J. M., et al. (2007). Social integration of robots into groups of cock- roaches to control self-organized choices. Science, 318(5853):1155–1158.

Han, D., Andersen, D. G., Kaminsky, M., Papagiannaki, K., and Seshan, S. (2009). Access point localization using local signal strength gradient. Passive and Active Network Measurement, pages 99–108.

Bibliography Haruno, M., Wolpert, D., and Kawato, M. (2001). Mosaic model for sensorimotor learning

and control. Neural computation, 13(10):2201–2220.

Haruno, M., Wolpert, D. M., and Kawato, M. (1999). Multiple paired forward-inverse models for human motor learning and control. Advances in neural information processing systems, pages 31–37.

Hauert, S., Leven, S., Zufferey, J.-C., and Floreano, D. (2010a). Communication-based leashing of real flying robots. In Robotics and Automation (ICRA), 2010 IEEE International Conference on, pages 15–20. IEEE.

Hauert, S., Leven, S., Zufferey, J.-C., and Floreano, D. (2010b). Communication-based swarm- ing for flying robots. In Proceedings of the Workshop on Network Science and Systems Issues in Multi-Robot Autonomy, IEEE International Conference on Robotics and Automation. Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa.

Hauert, S., Leven, S., Zufferey, J.-C., and Floreano, D. (2010c). Communication-based swarm- ing for flying robots. In International Workshop on Self-Organized Systems, number LIS- POSTER-2010-001.

Hauert, S., Zufferey, J.-C., and Floreano, D. (2009). Evolved swarming without positioning information: an application in aerial communication relay. Autonomous Robots, 26(1):21– 32.

He, K., Zhang, X., Ren, S., and Sun, J. (2015). Delving deep into rectifiers: Surpassing human- level performance on imagenet classification. arXiv preprint arXiv:1502.01852.

Helbing, D., Farkas, I., and Vicsek, T. (2000). Simulating dynamical features of escape panic. Nature, 407(6803):487–490.

Hemelrijk, C. K. and Hildenbrandt, H. (2008). Self-organized shape and frontal density of fish schools. Ethology, 114(3):245–254.

Hemelrijk, C. K. and Kunz, H. (2005). Density distribution and size sorting in fish schools: an individual-based model. Behavioral Ecology, 16(1):178–187.

Heo, N. and Varshney, P. K. (2005). Energy-efficient deployment of intelligent mobile sensor networks. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 35(1):78–92.

Hesslow, G. (2012). The current status of the simulation theory of cognition. Brain research, 1428:71–79.

Higgins, F., Tomlinson, A., and Martin, K. M. (2009). Survey on security challenges for swarm robotics. In Autonomic and Autonomous Systems, 2009. ICAS’09. Fifth International Confer- ence on, pages 307–312. IEEE.

Hildenbrandt, H., Carere, C., and Hemelrijk, C. K. (2010). Self-organized aerial displays of thousands of starlings: a model. Behavioral Ecology, 21(6):1349–1359.

Hoerl, A. E. and Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthog- onal problems. Technometrics, 12(1):55–67.

Hoffmann, H. (2007). Perception through visuomotor anticipation in a mobile robot. Neural Networks, 20(1):22–33.

Hoffmann, H. and Möller, R. (2004). Action selection and mental transformation based on a chain of forward models. From Animals to Animats, 8:213–222.

Holland, O. and Goodman, R. (2003). Robots with internal models a route to machine con- sciousness? Journal of Consciousness Studies, 10(4-5):77–109.

Howard, A., Matarić, M. J., and Sukhatme, G. S. (2002). Mobile sensor network deployment using potential fields: A distributed, scalable solution to the area coverage problem. In Distributed Autonomous Robotic Systems 5, pages 299–308. Springer.

Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in science and engineering, 9(3):90–95.

Ito, M. (2008). Control of mental activities by internal models in the cerebellum. Nature Reviews Neuroscience, 9(4):304–313.

Jackson, J. D. (1962). Classical electrodynamics. Wiley.

Jacob, P. and Jeannerod, M. (2005). The motor theory of social cognition: a critique. Trends in cognitive sciences, 9(1):21–25.

Jacobi, N., Husbands, P., and Harvey, I. (1995). Noise and the reality gap: The use of simula- tion in evolutionary robotics. In Proceedings of the Third European Conference on Advances in Artificial Life,, pages 704–720. Springer.

Jiménez-González, A., Martínez-de Dios, J. R., and Ollero, A. (2011). An integrated testbed for cooperative perception with heterogeneous mobile and static sensors. Sensors, 11(12):11516–11543.

Jiménez-González, A., Martinez-de Dios, J. R., and Ollero, A. (2013). Testbeds for ubiquitous robotics: A survey. Robotics and Autonomous Systems, 61(12):1487–1501.

Jin, Z., Waydo, S., Wildanger, E. B., Lammers, M., Scholze, H., Foley, P., Held, D., and Murray, R. M. (2004). Mvwt-ii: The second generation caltech multi-vehicle wireless testbed. In American Control Conference, 2004. Proceedings of the 2004, volume 6, pages 5321–5326. IEEE.

Jones, E., Oliphant, T., Peterson, P., et al. (2001–). SciPy: Open source scientific tools for Python. [Online; accessed 2015-05-19].

Jones, J. A. and Keough, D. (2008). Auditory-motor mapping for pitch control in singers and nonsingers. Experimental brain research, 190(3):279–287.

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